38 citations · 84 across the 20 of their papers we have counts for
20 papers
Learning-Based Robust Bayesian Persuasion with Conformal Prediction Guarantees
Heeseung Bang, Andreas A. Malikopoulos
Classical Bayesian persuasion assumes that senders fully understand how receivers form beliefs and make decisions--an assumption that rarely holds when receivers possess private in…
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning
Weihao Sun, Heeseung Bang, Andreas A. Malikopoulos
In this paper, we present an adherence-aware reinforcement learning (RL) approach aimed at seeking optimal lane-changing recommendations within a semi-autonomous driving environmen…
Route Recommendations for Traffic Management Under Learned Partial Driver Compliance
Heeseung Bang, Jung-Hoon Cho, Cathy Wu +1
In this paper, we aim to mitigate congestion in traffic management systems by guiding travelers along system-optimal (SO) routes. However, we recognize that most theoretical approa…
Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confounding
Nishanth Venkatesh S., Heeseung Bang, Andreas A. Malikopoulos
In this paper, we expand the Bayesian persuasion framework to account for unobserved confounding variables in sender-receiver interactions. While traditional models assume that bel…
On Mobility Equity and the Promise of Emerging Transportation Systems
Heeseung Bang, Aditya Dave, Filippos N. Tzortzoglou +2
This paper introduces a mobility equity metric (MEM) for evaluating fairness and accessibility in multi-modal intelligent transportation systems. The MEM simultaneously accounts fo…
A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms
Armin Mokhtarian, Jianye Xu, Patrick Scheffe +11
Connected and automated vehicles and robot swarms hold transformative potential for enhancing safety, efficiency, and sustainability in the transportation and manufacturing sectors…
A Mobility Equity Metric for Multi-Modal Intelligent Transportation Systems
Heeseung Bang, Aditya Dave, Filippos N. Tzortzoglou +1
In this paper, we introduce a metric to evaluate the equity in mobility and a routing framework to enhance the metric within multi-modal intelligent transportation systems. The mob…
Safe Merging in Mixed Traffic with Confidence
Heeseung Bang, Aditya Dave, Andreas A. Malikopoulos
In this letter, we present an approach for learning human driving behavior, without relying on specific model structures or prior distributions, in a mixed-traffic environment wher…
A Framework for Effective AI Recommendations in Cyber-Physical-Human Systems
Aditya Dave, Heeseung Bang, Andreas A. Malikopoulos
Many cyber-physical-human systems (CPHS) involve a human decision-maker who may receive recommendations from an artificial intelligence (AI) platform while holding the ultimate res…
Optimal trajectory planning meets network-level routing: Integrated control framework for emerging mobility systems
Heeseung Bang, Andreas A. Malikopoulos
In this paper, we introduce a hierarchical decision-making framework for emerging mobility systems. Despite numerous studies focusing on optimizing vehicle flow, practical feasibil…
Routing in Mixed Transportation Systems for Mobility Equity
Heeseung Bang, Aditya Dave, Andreas A. Malikopoulos
This letter proposes a routing framework in mixed transportation systems for improving mobility equity. We present a strategic routing game that governs interactions between compli…
A Study of an Atomic Mobility Game With Uncertainty Under Prospect Theory
Ioannis Vasileios Chremos, Heeseung Bang, Aditya Dave +2
In this paper, we present a study of a mobility game with uncertainty in the decision-making of travelers and incorporate prospect theory to model travel behavior. We formulate a m…
A Hierarchical Approach to Optimal Flow-Based Routing and Coordination of Connected and Automated Vehicles
Heeseung Bang, Andreas A. Malikopoulos
This paper addresses the challenge of generating optimal vehicle flow at the macroscopic level. Although several studies have focused on optimizing vehicle flow, little attention h…
Re-Routing Strategy of Connected and Automated Vehicles Considering Coordination at Intersections
Heeseung Bang, Andreas A. Malikopoulos
In this paper, we propose a re-routing strategy for connected and automated vehicles (CAVs), considering coordination and control of all the CAVs in the network. The objective for…
Combined Optimal Routing and Coordination of Connected and Automated Vehicles
Heeseung Bang, Behdad Chalaki, Andreas A. Malikopoulos
In this letter, we consider a transportation network with a 100\% penetration rate of connected and automated vehicles (CAVs) and present an optimal routing approach that takes int…
Congestion-Aware Routing, Rebalancing, and Charging Scheduling for Electric Autonomous Mobility-on-Demand System
Heeseung Bang, Andreas A. Malikopoulos
In this paper, we investigate the problem of routing, rebalancing, and charging for electric autonomous mobility-on-demand systems concerning traffic congestion. We analyze the pro…
A Scalable Last-Mile Delivery Service: From Simulation to Scaled Experiment
Meera Ratnagiri, Clare O'Dwyer, Logan E. Beaver +3
In this paper, we investigate the problem of a last-mile delivery service that selects up to available vehicles to deliver packages from a centralized depot to delivery…
A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experiments
Behdad Chalaki, Logan E. Beaver, A M Ishtiaque Mahbub +2
Emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, and electric vehicles, provide the most intriguing opportunity for enabling users to bett…
A Digital Smart City for Emerging Mobility Systems
Raymond M. Zayas, Logan E. Beaver, Behdad Chalaki +2
The increasing demand for emerging mobility systems with connected and automated vehicles has imposed the necessity for quality testing environments to support their development. I…
Energy-Optimal Goal Assignment of Multi-Agent System with Goal Trajectories in Polynomials
Heeseung Bang, Logan Beaver, Andreas A. Malikopoulos
In this paper, we propose an approach for solving an energy-optimal goal assignment problem to generate the desired formation in multi-agent systems. Each agent solves a decentrali…